A. Field of Invention
This invention pertains to a method and apparatus adapted to monitor the intrinsic activity of a person to determine if the patient may be a candidate for cardiac disease. More specifically, the present invention pertains to a monitoring apparatus and method that analyzes the intrinsic cardiac signals from a patient to detect T-wave alternans, and use the same to derive an indication of the patient's cardiac condition.
B. Description of the Prior Art
One of the major objectives in cardiology is the identification of individuals who are prone to sudden cardiac electrical disturbances and whose hearts are electrically unstable. Proper and earlier identification of these individuals leads to a designation of these patients as potential candidates for either implantable or external cardioversion/defibrillation devices. Mass screening of individuals is instrumental in detecting the individuals with potential cardiac problems. Standard electrophysiological studies, though effective, usually involve invasive procedures [See Rosenbaum et al., 1996].
In the last few years, it has been found that T-wave alternans analysis is an effective method to predict the cardiac vulnerability to ventricular arrhythmia and sudden cardiac death. Three different approaches have been suggested for this analysis and several clinical and animal studies have validated its benefits. In a first approach, a group at MIT used a spectral method for estimation of T-wave alternans (TWA). Spectral analysis seems to indicate a definite peak at alternans frequency, despite the absence of visible ST/T-wave alternans on the surface ECG. In the original method by Smith et al., 1988, multiple spectra are generated during the analysis, each corresponding to a different part of the T wave. The signal registered at 0.5 cycles/beat indicates the ECG alternans and its magnitude quantifies the degree of alternans. Apparently a minimum heart rate of 100 beats/minute is needed for reliable detection of TWA using this technique. The respiration frequency peak is relatively variable and varies between 0.15 cycles/beat to 0.35 cycles/beat and is bound to create noise. Alternans measurement is done with reference to white noise/random noise, which is distributed through out the spectrum. The alternans voltage VAlt is calculated as follows:VAlt=(S0.5−SNoise)1/2
VAlt is measured in microvolts and S0.5 and SNoise are the magnitudes of power spectrum at 0.5 cycles/beat and reference noise band respectively and are measured in microvolts squared. This value is normalized and compared with noise voltage to produce a unique measure (See U.S. Pat. No. 5,713,367, incorporated herein by reference).
However, there are several problems associated with this method. For example, the method is unable to localize the phase changes. An inherent problem in Fourier transform based methods is their inability to distinguish the noise or harmonics of the noise occurring at 0.5 cycles/beat. Several adjustments and pre-processing methods have been attempted to overcome these problems, including a known system of averaging signals between the different electrodes to remove the common motion artifact noise.
Problems related to the ectopic beats result in the phase reversals frequently which produces a decrease in spectral measurements due to the change in alternans pattern from an ABAB . . . type to a BABA . . . type pattern, where A and B may represent higher and lower values of T wave peak amplitude in a beat or any other similar parameters.
In another method suggested by Verrier R L and B D Nearing in 1992, a complex demodulation algorithm is used which assumes that the T-wave is a sinusoid of slowly varying amplitude at a frequency equal to alternans frequency and phase. The period from 60 to 290 ms following the apex of each R-wave was determined to coincide with the location of the T-wave. This period was divided into bins 10 ms wide for each successive beat, and the area between the ECG and the iso-electric baseline was computed for each 10 ms interval. A sixteenth order Butterworth filter was used for both de-trending and demodulating to remove the large low-frequency variation in T-wave area that occurs during occlusion and to leave a cleaner signal for spectral analysis. Several of the drawbacks in spectral analysis of the TWA are removed in this method and the complex demodulation takes into account the non-stationarity present in a time series.
Clinical studies indicate that the first half of a typical T-wave is much more sensitive to T-wave alternans then the second half (U.S. Pat. Nos. 5,842,997; 5,921,940). This factor combined with the fact that it is easier to detect T wave peaks than T wave boundaries make parameters like T wave peak amplitude and the area around the peak well suited for TWA analysis. Moreover, the estimation of such factors is less prone to delineation errors associated with T wave boundary errors.
Both these methods start with the assumption of sinusoidal varying alternans components and hence are different from the actual periodicity. The assumption of sinusoidal variations leads to the problems of harmonics of motion and respiration components interfering with this higher frequency component at 0.5 cycles/beat. In addition this technique utilizes elaborate filtering schemes and the entire processing results in a time domain equivalent of the spectral approach.
The third method of alternans estimation developed by Burattini et al., in 1995 is based upon the correlation analysis of individual beats relative to a median beat. This is also a time domain equivalent of spectral estimation and considered to be less in under-estimation compared to spectral methods. Time localization is possible in auto-correlation based methods, compared to the inability of frequency domain methods. However, the pre-processing needed for this method is as demanding as the other approaches discussed above.
Extensive clinical studies lead to the following conclusions regarding TWA.
(1) T-wave alternans are better predictors of ventricular arrhythmias than the potential ECG analysis and HRV analysis methods presently used.
(2) Patients with a positive microvolt T-wave alternans test were 13.9 times more likely to have a serious ventricular arrhythmia or to die than patients with a negative microvolt T wave alternans test.
(3) TWA analysis results are comparable to the electrophysiology studies relying on invasive procedures.
(4) The precise cellular and ionic basis for TWA is not precisely understood. Preliminary results indicate that beat-to-beat variations in action potential duration and action recovery interval are the cause of the TWA in surface electrocardiograms (Verrier and Nearing, 1994).
(5) T-wave alternans relate to a subtle change in the T-wave morphology that occurs in each alternate beat. TWA appear to reflect the occurrence of localized action-potential alternans, which creates dispersion of recovery, which in turn promotes the development of re-entrant arrhythmias.
In the last few years, several non-invasive methodologies have been suggested for predicting ventricular malignant arrhythmias. These methods include high frequency signal-averaged electrocardiography (SAECG) for late-potential analysis, heart rate variability and QT dispersion analysis (Gomes J et a., 1991; Day C P et al., 1990; Task Force of the ESC and the NASPE, 1996). These methods are limited in sensitivity and specificity in screening high-risk patients for ventricular arrhythmias and sudden cardiac death (SCD). Compared to these methods, T wave alternans have proved to be more reliable estimators and perform as well as invasive, electrophysiological studies in risk stratifying patients for life-threatening arrhythmias [Gold M et al., 2000].
Normal hearts exhibit alternans during very high heart rate, but diseased ones exhibit alternans, even during normal sinus heart rates. T wave alternans form part of repolarization alternans, while QRS alternans form part of depolarization alternans. QRS alternans show more correlation with heart rate and not with cardiac vulnerability. On the other hand, repolarization alternans, especially TWA, which primarily involves ST and/or T wave, have exhibited a consistent relationship with ventricular arrhythmias. Compared to other predictors of ventricular arrhythmias, TWA appears to be the only non-invasive technique having an efficacy comparable to that of electrophysiology labs. According to Rosenbaum DS et al., 1996, the challenge is to derive appropriate methodologies to detect “microscopic” T wave alternans in patients.
The fundamental premise in all three previous approaches is that TWA are related to the measurement of repetitions in the frequency or equivalent correlation domains. However, computational cost of these methods makes them difficult for real-time update with new beats. The time localization abilities of spectral domain methods is inferior compared to dynamic, time domain estimations. There is also a possibility that due to ectopic beats, a pattern reversal (from ABABAB . . . to BABABA . . . ) may occur. However, there is no efficient way to distinguish such a reversal in the frequency domain. The change in amplitude of the spectrum is related to both the duration and the amplitude of the alternans in an episode. In other words, the amount of alternation detected using the frequency domain methods is subject to the baseline/static portion of the time series (See U.S. Pat. Nos. 5,713,367; 5570696, incorporated herein by reference).